Quasi-error notifications in a dispersed storage network

ABSTRACT

Methods for use in a dispersed storage network (DSN) to retrieve encoded data from memory device of an impaired storage unit. In various embodiments, a computing device of the DSN issues requests to a plurality of storage units, including the impaired storage unit, to recover at least a decode threshold number of encoded data slices of a set of encoded data slices. When the impaired storage unit determines that it is not able to quickly retrieve the requested data slice for provision to the computing device, the impaired storage unit promptly issues a quasi-error response instead. In response to receiving less than the decode threshold number of encoded data slices and a quasi-error response, the computing device determines to issue another slice request(s) to another storage unit(s) and/or issue a continue request instructing the impaired storage unit to continue processing the request to recover the data slice stored therein.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 120 as a continuation of U.S. Utility application Ser. No.15/457,408, entitled “QUASI-ERROR NOTIFICATIONS IN A DISPERSED STORAGENETWORK”, filed Mar. 13, 2017, which is a continuation-in-part of U.S.Utility application Ser. No. 15/058,408, entitled “ACCESSING COMMON DATAIN A DISPERSED STORAGE NETWORK”, filed Mar. 2, 2016 and now issued asU.S. Pat. No. 10,037,171, which in turn claims priority pursuant to 35U.S.C. § 119(e) to U.S. Provisional Application No. 62/154,886, entitled“BALANCING MAINTENANCE AND ACCESS TASKS IN A DISPERSED STORAGE NETWORK”,filed Apr. 30, 2015, all of which are hereby incorporated herein byreference in their entirety and made part of the present U.S. Utilitypatent application for all purposes.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks, and moreparticularly to recovery of data from impaired storage units of adispersed storage network.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on a remote storagesystem. The remote storage system may include a RAID (redundant array ofindependent disks) system and/or a dispersed storage system that uses anerror correction scheme to encode data for storage.

In a RAID system, a RAID controller adds parity data to the originaldata before storing it across an array of disks. The parity data iscalculated from the original data such that the failure of a single disktypically will not result in the loss of the original data. While RAIDsystems can address certain memory device failures, these systems maysuffer from effectiveness, efficiency and security issues. For instance,as more disks are added to the array, the probability of a disk failurerises, which may increase maintenance costs. When a disk fails, forexample, it needs to be manually replaced before another disk(s) failsand the data stored in the RAID system is lost. To reduce the risk ofdata loss, data on a RAID device is often copied to one or more otherRAID devices. While this may reduce the possibility of data loss, italso raises security issues since multiple copies of data may beavailable, thereby increasing the chances of unauthorized access. Inaddition, co-location of some RAID devices may result in a risk of acomplete data loss in the event of a natural disaster, fire, powersurge/outage, etc.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentdisclosure;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present disclosure;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present disclosure;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present disclosure;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present disclosure;

FIG. 6 is a schematic block diagram of an example of slice naminginformation for an encoded data slice (EDS) in accordance with thepresent disclosure;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present disclosure;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present disclosure;

FIG. 9 is a schematic block diagram of another embodiment of a DSNperforming recovery of stored data from an impaired storage unit inaccordance with the present disclosure; and

FIG. 10 is a logic diagram illustrating an example of recovery of storeddata in accordance with the present disclosure when retrieval isdegraded.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed storage (DS) error encoded data.

Each of the storage units 36 is operable to store DS error encoded dataand/or to execute (e.g., in a distributed manner) maintenance tasksand/or data-related tasks. The tasks may be a simple function (e.g., amathematical function, a logic function, an identify function, a findfunction, a search engine function, a replace function, etc.), a complexfunction (e.g., compression, human and/or computer language translation,text-to-voice conversion, voice-to-text conversion, etc.), multiplesimple and/or complex functions, one or more algorithms, one or moreapplications, maintenance tasks (e.g., rebuilding of data slices,updating hardware, rebooting software, restarting a particular softwareprocess, performing an upgrade, installing a software patch, loading anew software revision, performing an off-line test, prioritizing tasksassociated with an online test, etc.), etc.

Each of the computing devices 12-16, the managing unit 18, integrityprocessing unit 20 and (in various embodiments) the storage units 36include a computing core 26, which includes network interfaces 30-33.Computing devices 12-16 may each be a portable computing device and/or afixed computing device. A portable computing device may be a socialnetworking device, a gaming device, a cell phone, a smart phone, adigital assistant, a digital music player, a digital video player, alaptop computer, a handheld computer, a tablet, a video game controller,and/or any other portable device that includes a computing core. A fixedcomputing device may be a computer (PC), a computer server, a cableset-top box, a satellite receiver, a television set, a printer, a faxmachine, home entertainment equipment, a video game console, and/or anytype of home or office computing equipment. Note that each of themanaging unit 18 and the integrity processing unit 20 may be separatecomputing devices, may be a common computing device, and/or may beintegrated into one or more of the computing devices 12-16 and/or intoone or more of the storage units 36.

Each interface 30, 32 and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 and 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data (e.g., data object 40) as subsequently describedwith reference to one or more of FIGS. 3-8. In this example embodiment,computing device 16 functions as a dispersed storage processing agentfor computing device 14. In this role, computing device 16 dispersedstorage error encodes and decodes data on behalf of computing device 14.With the use of dispersed storage error encoding and decoding, the DSN10 is tolerant of a significant number of storage unit failures (thenumber of failures is based on parameters of the dispersed storage errorencoding function) without loss of data and without the need for aredundant or backup copies of the data. Further, the DSN 10 stores datafor an indefinite period of time without data loss and in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The managing unit 18 creates and stores user profile information (e.g.,an access control list (ACL)) in local memory and/or within memory ofthe DSN memory 22. The user profile information includes authenticationinformation, permissions, and/or the security parameters. The securityparameters may include encryption/decryption scheme, one or moreencryption keys, key generation scheme, and/or data encoding/decodingscheme.

The managing unit 18 creates billing information for a particular user,a user group, a vault access, public vault access, etc. For instance,the managing unit 18 tracks the number of times a user accesses anon-public vault and/or public vaults, which can be used to generateper-access billing information. In another instance, the managing unit18 tracks the amount of data stored and/or retrieved by a user deviceand/or a user group, which can be used to generate per-data-amountbilling information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation/access requests (e.g., readand/or write requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10. Examplesof load balancing, service differentiation and dynamic resourceselection for data access operations are discussed in greater detailwith reference to FIGS. 9-13.

To support data storage integrity verification within the DSN 10, theintegrity processing unit 20 (and/or other devices in the DSN 10) mayperform rebuilding of ‘bad’ or missing encoded data slices. At a highlevel, the integrity processing unit 20 performs rebuilding byperiodically attempting to retrieve/list encoded data slices, and/orslice names of the encoded data slices, from the DSN memory 22.Retrieved encoded slices are checked for errors due to data corruption,outdated versioning, etc. If a slice includes an error, it is flagged asa ‘bad’ or ‘corrupt’ slice. Encoded data slices that are not receivedand/or not listed may be flagged as missing slices. Bad and/or missingslices may be subsequently rebuilt using other retrieved encoded dataslices that are deemed to be good slices in order to produce rebuiltslices. A multi-stage decoding process may be employed in certaincircumstances to recover data even when the number of valid encoded dataslices of a set of encoded data slices is less than a relevant decodethreshold number. The rebuilt slices may then be written to DSN memory22. Note that the integrity processing unit 20 may be a separate unit asshown, included in DSN memory 22, included in the computing device 16,and/or distributed among the storage units 36.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment (i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of five, a decode threshold ofthree, a read threshold of four, and a write threshold of four. Inaccordance with the data segmenting protocol, the computing device 12 or16 divides the data (e.g., a file (e.g., text, video, audio, etc.), adata object, or other data arrangement) into a plurality of fixed sizeddata segments (e.g., 1 through Y of a fixed size in range of Kilo-bytesto Tera-bytes or more). The number of data segments created is dependentof the size of the data and the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar width number (T) of five and decode threshold number ofthree. In this example, a first data segment is divided into twelve datablocks (D1-D12). The coded matrix includes five rows of coded datablocks, where the first row of X11-X14 corresponds to a first encodeddata slice (EDS 1_1), the second row of X21-X24 corresponds to a secondencoded data slice (EDS 2_1), the third row of X31-X34 corresponds to athird encoded data slice (EDS 3_1), the fourth row of X41-X44corresponds to a fourth encoded data slice (EDS 4_1), and the fifth rowof X51-X54 corresponds to a fifth encoded data slice (EDS 5_1). Notethat the second number of the EDS designation corresponds to the datasegment number. In the illustrated example, the value X11=aD1+bD5+cD9,X12=aD2+bD6+cD10, . . . X53=mD3+nD7+oD11, and X54=mD4+nD8+oD12.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as at least part of a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

In order to recover a data segment from a decode threshold number ofencoded data slices, the computing device uses a decoding function asshown in FIG. 8. As shown, the decoding function is essentially aninverse of the encoding function of FIG. 4. The coded matrix includes adecode threshold number of rows (e.g., three in this example) and thedecoding matrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2and 4, and then inverted to produce the decoding matrix.

Typically, when memory is quarantined or in an otherwise impaired state,the storage unit holding that memory may respond to a data accessrequest by returning an error rather than service the request.Alternatively, the storage unit may attempt to service the request butdo so very slowly (due to the impaired memory). Such actions mayeffectively delay processing of the data access request. As describedmore fully below in conjunction with the novel examples of FIGS. 9 and10, a “quasi-error” response is introduced for use when retrieving datafrom an impaired memory. Briefly, an impaired storage unit mayimmediately return a quasi-error response to a data access request(e.g., a read data or write data request) involving impaired memory(e.g., memory that is not able to produce an encoded date slice in arelatively timely manner due to network conditions, degraded memory,heavy load conditions, maintenance operations, quarantine, etc.). Acomputing device receiving the quasi-error response may elect to issue anew data access request to another storage unit(s) and/or may issue a“continue request” command to the impaired storage unit such that thestorage unit continues to attempt to process the data access request,even if a there is high probability of continued delay or failure.

In another example, a storage unit issuing a “quasi-error” response to adata access request may be configured to continue processing the requestby default. During continued processing of the request by the impairedstorage unit, a computing device receiving the quasi-error mayconcurrently attempt to recover impacted data (e.g., a threshold numberof encoded data slices) from other storage units. If sufficient data isrecovered from the other storage units, or the computing deviceotherwise determines that continued processing of the original dataaccess request is unnecessary or undesirable, the computing device mayissue a “cancel request” command to the impaired storage unit to stopprocessing of the data access request.

Referring now to FIG. 9, a schematic block diagram of another embodimentof a DSN performing recovery of stored data from an impaired storageunit in accordance with the present disclosure is shown. The illustratedDSN includes the computing device 16 of FIG. 1, the network 24 of FIG.1, and a storage unit set 82. The storage unit set 82 includes a set ofstorage units 82 a-82 h. Each storage unit may be implemented utilizingthe storage unit 36 of FIG. 1, and each of the storage units includes aDS client module 34, a processing module and memory (not separatelyillustrated). Hereafter, the storage unit set may be interchangeablyreferred to as a set of storage units. The storage units of a storageset may be located at a same physical location (site) or located atmultiple physical locations without departing from the technology asdescribed herein. The DSN functions to recover stored data when dataretrieval is degraded.

The storage unit set 82 may include a number of storage units inaccordance with dispersal parameters of a dispersed storage error codingfunction, where a data segment of data is dispersed storage error codedin accordance with the dispersed storage error coding function utilizingthe dispersal parameters to create a set of encoded data slices. Forexample, the storage unit set 82 includes storage units 82 a-82 h when awidth of the dispersal parameters is 8. The dispersal parameters furtherinclude a decode threshold number, where a decode threshold number(e.g., 5 when the width is 8) of encoded data slices of the set ofencoded data slices is a minimum number of encoded data slices typicallyrequired to recover the data segment. The data that is encoded into theDS error encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or indexing and key information for usein dispersed storage operations.

In an example of operation of the recovering of the stored data, thecomputing device 16 issues a decode threshold number of slice requeststo storage units of the storage unit set 82. Issuing the slice requestsmay include one or more of receiving a retrieve data request, selectingthe storage units, generating the read slice requests, and sending, viathe network 24, the generated read slice requests to the selectedstorage units. For example, the computing device 16 generates and sends,via the network 24, slice requests 84 a-84 e to the storage units 82a-82 e.

Having sent the slice requests, the computing device 16 receivesresponses within a response timeframe, where the responses includes lessthan a decode threshold number of encoded data slices and at least onequasi-error response. For example, the computing device 16 receives readslice responses from storage units 82 a, 82 c, 82 d and 82 e thatinclude encoded data slices 86 a, 86 c, 86 d and 86 e, respectively, andreceives a quasi-error response 88 from the impaired storage unit 82 bwhen the storage unit 82 b has impaired memory and is unable toimmediately (e.g., within a relatively short period of time in relationto a typical response time of the storage unit) send the encoded dataslice 86 b to the computing device 16.

Having received the responses, the computing device 16 determineswhether to utilize at least one other storage unit of the storage unitset (e.g., one of storage units 82 f-82 h). Such a determination may bebased on one or more of: the number of error responses (e.g.,quasi-error responses, slice unavailable responses); an estimated timeto retrieve another encoded data slice; a timing requirement; and thenumber of other available storage units of the storage unit set. Forexample, the computing device 16 determines not to utilize anotherstorage unit when receiving the quasi-error response with regards to theencoded data slice 86 b and the storage units 82 f-82 h are determinedto be offline or unreachable via the network 24.

When not utilizing another storage unit, the computing device 16 in oneexample issues at least one continue request to a storage unitcorresponding to at least one quasi-error response. For example, thecomputing device 16 issues, via the network 24, a continue request 90 tothe storage unit 82 b with regards to the encoded data slice 86 b. Inresponse, the storage unit 82 b continues to process the slice request(if not already doing so as described below) and proceeds to send, viathe network 24, the encoded data slice 86 b to the computing device 16.When the storage unit 82 b is unable to retrieve the encoded data slice86 b (e.g., after a threshold amount of time has elapsed), storage unit82 b may send an error response to the computing device 16. Whenreceiving the decode threshold number of encoded data slices, thecomputing device 16 disperse storage error decodes the decode thresholdnumber of encoded data slices to reproduce a data segment of the dataobject of the retrieve data request.

Alternatively, or in addition to, the computing device 16 issues anotherslice request to another storage unit to produce the decode thresholdnumber of encoded data slices. For example, the computing device 16substantially simultaneously sends the continue request 90 to thestorage unit 82 b and sends a slice request 84 f to the storage unit 82f (or a storage unit of another set of storage units if copies of therelevant encoded data slices are stored elsewhere) with regards toretrieving the encoded data slice 86 f, receives at least one of theencoded data slice 86 b and the encoded data slice 86 f to complete thedecode threshold number of encoded data slices, and disperse storageerror decodes the decode threshold number of encoded data slices toreproduce the data segment. In another example, after issuing aquasi-error response, the storage units 82 a-82 h may be configured toautomatically continue processing a data access request until a cancelrequest 92 is received or a final error response is issued (e.g.,continued processing of slice request 84 b by storage unit 82 b is notcontingent on receipt of continue request 90).

In another example of reading encoded data slices of a stored set ofencoded data slices having a decode threshold number of 10 and a pillarwidth of 15, wherein 10 slice requests are issued to a set of 15 storageunits and 9 data slices and 1 quasi-error response are received, arequesting device may determine to issue an additional slice request toone or more of the remaining 5 storage units. A continue request mayalso be issued to the impaired storage unit if it is not configured toautomatically continue processing the slice relevant slice request, or acancel request may be issued to stop processing of the slice requestwhen an additional slice is available or likely to be available. If,however, the remaining 5 storage units are unavailable (e.g., offline),the requesting device will generally request continued processing of theslice request by the impaired storage unit (or refrain from issuing acancel request) in an attempt to recover the decode threshold number ofencoded data slices.

FIG. 10 is a logic diagram illustrating an example of recovery of storeddata in accordance with the present disclosure when retrieval isdegraded. The method includes step 100 where a processing module (e.g.,of a computing device 16) issues at least a decode threshold number ofslice requests to storage units of a storage unit set. For example, theprocessing module selects the storage units, generates the slicerequests, and sends the slice requests to the selected storage units.

The method continues at step 102 where the processing module receivesresponses that include less than a decode threshold number of encodeddata slices and at least one quasi-error response. The method continuesat step 104 where the processing module determines whether to utilizeanother storage unit of the storage unit set. For example, theprocessing module determines not to utilize another storage unit when acalculated probability of recovering an incremental encoded data slicefrom the other storage unit is a calculated probability of recovering anencoded data slice from a storage unit associated with the at least onequasi-error response, or the calculated probability of recovering anencoded data slice from a storage unit associated with the at least onequasi-error response is lower than threshold value. Alternatively, theprocessing module determines not to utilize another storage unit whenslice requests were initially issued to all storage units of a storageset.

When not utilizing another storage unit, the method continues at step106 where the processing module issues at least one continue request tothe storage unit corresponding to the at least one quasi-error response(or, alternatively, defers issuing a cancel request when the storageunit is configured to automatically continue processing a slice requestafter issuing a quasi-error). For example, the processing moduleidentifies the storage unit, generates the continue request (e.g., toinclude a status code for the continue request and a slice nameassociated with encoded data slice), and sends the continue request tothe identified storage unit. When receiving the decode threshold numberof encoded data slices, the method continues at step 108 where theprocessing module dispersed storage error decodes the decode thresholdnumber of encoded data slices to reproduce a data segment of a dataobject for retrieval.

The methods described above in conjunction with the computing device 16and storage units can alternatively be performed by other modules (e.g.,DS client modules 34) of a dispersed storage network or by other devices(e.g., managing unit 18 or integrity processing unit 20). Anycombination of a first module, a second module, a third module, a fourthmodule, etc. of the computing devices and the storage units may performthe method described above. In addition, at least one memory section(e.g., a first memory section, a second memory section, a third memorysection, a fourth memory section, a fifth memory section, a sixth memorysection, etc. of a non-transitory computer readable storage medium) thatstores operational instructions can, when executed by one or moreprocessing modules of one or more computing devices and/or by thestorage units of the dispersed storage network (DSN), cause the one ormore computing devices and/or the storage units to perform any or all ofthe method steps described above.

As may be used herein, the terms “substantially” and “approximately”provide an industry-accepted tolerance for its corresponding term and/orrelativity between items. Such an industry-accepted tolerance rangesfrom less than one percent to fifty percent. Such relativity betweenitems ranges from a difference of a few percent to magnitudedifferences. As may also be used herein, the term(s) “configured to”,“operably coupled to”, “coupled to”, and/or “coupling” includes directcoupling between items and/or indirect coupling between items via anintervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for an exampleof indirect coupling, the intervening item does not modify theinformation of a signal but may adjust its current level, voltage level,and/or power level. As may further be used herein, inferred coupling(i.e., where one element is coupled to another element by inference)includes direct and indirect coupling between two items in the samemanner as “coupled to”. As may even further be used herein, the term“configured to”, “operable to”, “coupled to”, or “operably coupled to”indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from Figureto Figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information. A computer readable memory/storage medium,as used herein, is not to be construed as being transitory signals perse, such as radio waves or other freely propagating electromagneticwaves, electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), the DSN including a set of storage units, the method comprises:issuing slice requests to a plurality of storage units of the set ofstorage units for at least a decode threshold number of encoded dataslices of a set of encoded data slices, wherein a data segment of a dataobject is dispersed storage error encoded to produce the set of encodeddata slices, and wherein a decode threshold number of encoded dataslices of the set of encoded data slices is required to recover the datasegment; receiving a quasi-error response from a first storage unit ofthe plurality of storage units receiving the slice requests; and inresponse to the quasi-error response from the first storage unit,issuing a continue request to the first storage unit, the continuerequest requesting continued processing of a corresponding slicerequest.
 2. The method of claim 1 further comprises: in response to thequasi-error response, issuing an additional slice request, to anadditional storage unit of the set of storage units, for an encoded dataslice of the set of encoded data slices.
 3. The method of claim 2further comprises: receiving the decode threshold number of encoded dataslices in response to the continue request or the additional slicerequest; and dispersed storage error decoding the decode thresholdnumber of encoded data slices to recover the data segment.
 4. The methodof claim 2, wherein at least one of issuing the continue request orissuing the additional slice request is further based on: a number ofquasi-error responses received from the plurality of storage units ofthe set of storage units; and a number of additional storage units ofthe set of storage units to which a slice request was not issued.
 5. Themethod of claim 2, wherein issuing the additional slice request isfurther based on at least one of: determining that a calculatedprobability of receiving an encoded data slice from the first storageunit is below a threshold level; or determining that a calculatedprobability of receiving an encoded data slice from the additionalstorage unit is greater than a calculated probability of receiving anencoded data slice from the first storage unit.
 6. The method of claim2, wherein issuing the additional slice request is further based ondetermining at least one of: an estimated time to retrieve an encodeddata slice from the additional storage unit; or a timing requirement. 7.The method of claim 1, wherein the continue request includes a statuscode and a slice name of an encoded data slice.
 8. The method of claim1, wherein issuing slice requests to the plurality of storage units ofthe set of storage units includes: receiving a retrieve data requestassociated with the data object; and selecting the plurality of storageunits based on the retrieve data request.
 9. A computing device of agroup of computing devices of a dispersed storage network (DSN), the DSNincluding a set of storage units, the computing device comprises: anetwork interface; a local memory; and a processing module operablycoupled to the network interface and the local memory, wherein theprocessing module is configured to: issue, via the network interface,slice requests to a plurality of storage units of the set of storageunits for at least a decode threshold number of encoded data slices of aset of encoded data slices, wherein a data segment of a data object isdispersed storage error encoded to produce the set of encoded dataslices, and wherein a decode threshold number of encoded data slices ofthe set of encoded data slices is required to recover the data segment;receive, via the network interface, a plurality of responses from theplurality of storage units, wherein the plurality of responses includesa quasi-error response from a first storage unit of the plurality ofstorage units receiving the slice requests; and in response to thequasi-error response, issue, via the network interface, a continuerequest to the first storage unit, the continue request requestingcontinued processing of a corresponding slice request.
 10. The computingdevice of claim 9, wherein the processing module is further configuredto: in response to the quasi-error response, issue, via the networkinterface, an additional slice request to an additional storage unit ofthe set of storage units.
 11. The computing device of claim 10, whereinthe processing module is further configured to: receive, via the networkinterface, the decode threshold number of encoded data slices inresponse to the continue request or the additional slice request; anddispersed storage error decode the decode threshold number of encodeddata slices to recover the data segment.
 12. The computing device ofclaim 11, wherein the processing module is further configured to: whenreceiving the decode threshold number of encoded data slices in responseto the additional slice request, issue, via the network interface, acancel request to the first storage unit, the cancel request requestingdiscontinued processing of the corresponding slice request.
 13. Thecomputing device of claim 10, wherein at least one of issuing thecontinue request or issuing the additional slice request is furtherbased on: a number of quasi-error responses received from the pluralityof storage units of the set of storage units; or a number of additionalstorage units of the set of storage units to which a slice request wasnot issued.
 14. The computing device of claim 10, wherein issuing theadditional slice request is further based on at least one of:determining that a calculated probability of receiving an encoded dataslice from the first storage unit is below a threshold level; ordetermining that a calculated probability of receiving an encoded dataslice from the additional storage unit is greater than a calculatedprobability of receiving an encoded data slice from the first storageunit.
 15. The computing device of claim 10, wherein issuing theadditional slice request is further based on at least one of: anestimated time to retrieve an encoded data slice from the additionalstorage unit; or a timing requirement.
 16. The computing device of claim9, wherein issuing slice requests to the plurality of storage units ofthe set of storage units includes: receiving, via the network interface,a retrieve data request associated with the data object; and selectingthe plurality of storage units based on the retrieve data request.
 17. Amethod for execution by one or more processing modules of a storage unitof a set of storage units of a dispersed storage network (DSN), the DSNincluding a set of storage units storing encoded data, the methodcomprises: storing, in a memory device of the storage unit, an encodeddata slice of a set of encoded data slices, wherein segments of a dataobject are dispersed storage error encoded to produce a plurality ofsets of encoded data slices including the set of encoded data slices;receiving, from a computing device of the DSN, a slice requestcorresponding to the encoded data slice for processing by the storageunit; determining whether the encoded data slice is immediatelyavailable to send to the computing device; and in response todetermining that the encoded data slice is not immediately available tosend to the computing device, sending a quasi-error response to thecomputing device.
 18. The method of claim 17 further comprising:receiving, from the computing device, a continue request in reply to thequasi-error response; and in response to the continue request,continuing processing of the slice request.
 19. The method of claim 18further comprising: receiving a cancel request in reply to thequasi-error response; and in response to the cancel request,discontinuing processing of the slice request.
 20. The method of claim18, wherein continuing processing of the slice request includes:determining that the encoded data slice is unavailable from the memorydevice of the storage unit; and sending an error response to thecomputing device.